MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2019) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.

Package details

AuthorKeefe Murphy [aut, cre] (<https://orcid.org/0000-0002-7709-3159>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>)
MaintainerKeefe Murphy <keefe.murphy@ucd.ie>
LicenseGPL (>= 2)
Version1.3.0
URL https://cran.r-project.org/package=MoEClust
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("MoEClust")

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MoEClust documentation built on April 14, 2020, 7:12 p.m.